Papers by Sungnyun Kim
DocKD: Knowledge Distillation from LLMs for Open-World Document Understanding Models (2024.emnlp-main)
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Sungnyun Kim, Haofu Liao, Srikar Appalaraju, Peng Tang, Zhuowen Tu, Ravi Satzoda, R. Manmatha, Vijay Mahadevan, Stefano Soatto
| Challenge: | Existing methods for visual document understanding are limited by training on a small-scale, curated document dataset, compromising generalizability of VDU models to diverse documents. |
| Approach: | They propose a framework that integrates external document knowledge into the data generation process. |
| Outcome: | The proposed framework produces high-quality annotations and surpasses direct knowledge distillation approach. |
Two Heads Are Better Than One: Audio-Visual Speech Error Correction with Dual Hypotheses (2026.findings-acl)
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| Challenge: | Recent advances have introduced GER frameworks that utilize LLMs to refine ASR outputs. |
| Approach: | They propose a framework that allows a large language model to compose independent N-best hypotheses from separate automatic speech recognition (ASR) and visual speech recognition models. |
| Outcome: | The proposed framework achieves 57.7% error rate gain over standard ASR baseline, compared to single-stream approaches that achieve only 10% gain. |